Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease

Genome Medicine

Christopher Bartlett¹  Siwen Xu²  Chuanpeng Dong²  Yunlong Liu³  Yan Zhang⁴  Xiaoqing Huang⁵  Tongxin Wang⁶  Yijie Wang⁶  Jie Zhang⁷  Mohammad Abu Zaid⁸  Wei Shao⁸  Christina Y. Yu⁹  Travis S. Johnson^1,10  Kun Huang^1,11  Brian A. Walker^1,12  Zhi Huang^1,13 

[1] Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, 575 Children’s Crossroad, 43215, Columbus, OH, USA;Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 W. 10th St, Suite 5000, 46202, Indianapolis, IN, USA;Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 W. 10th St, Suite 5000, 46202, Indianapolis, IN, USA;Department of Medical and Molecular Genetics, Indiana University School of Medicine, 410 W 10th St, Suite 4000, 46202, Indianapolis, IN, USA;Department of Biomedical Informatics, The Ohio State University College of Medicine, 370 W 9th Ave, 43210, Columbus, OH, USA;The Ohio State University Comprehensive Cancer Center (OSUCCC - James), Starling-Loving Hall, 320 W 10th Ave, 43210, Columbus, OH, USA;Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 W 10th St, Suite 3000, 46202, Indianapolis, IN, USA;Department of Computer Science, Indiana University, 150 S Woodlawn Ave, 47405, Bloomington, IN, USA;Department of Medical and Molecular Genetics, Indiana University School of Medicine, 410 W 10th St, Suite 4000, 46202, Indianapolis, IN, USA;Department of Medicine, Indiana University School of Medicine, 535 Barnhill Dr, 46202, Indianapolis, IN, USA;Department of Medicine, Indiana University School of Medicine, 535 Barnhill Dr, 46202, Indianapolis, IN, USA;Department of Biomedical Informatics, The Ohio State University College of Medicine, 370 W 9th Ave, 43210, Columbus, OH, USA;Department of Medicine, Indiana University School of Medicine, 535 Barnhill Dr, 46202, Indianapolis, IN, USA;Department of Biomedical Informatics, The Ohio State University College of Medicine, 370 W 9th Ave, 43210, Columbus, OH, USA;Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 W 10th St, Suite 3000, 46202, Indianapolis, IN, USA;Department of Medicine, Indiana University School of Medicine, 535 Barnhill Dr, 46202, Indianapolis, IN, USA;Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 W 10th St, Suite 3000, 46202, Indianapolis, IN, USA;Department of Medical and Molecular Genetics, Indiana University School of Medicine, 410 W 10th St, Suite 4000, 46202, Indianapolis, IN, USA;Regenstrief Institute, 1101 W 10th St, 46202, Indianapolis, IN, USA;Division of Hematology Oncology, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, 535 Barnhill Dr, 46202, Indianapolis, IN, USA;School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Ave, 47907, West Lafayette, IN, USA;

关键词: Prognostic models;    Survival;    Cox proportional hazards;    Single-cell RNA sequencing;    scRNA-seq;    Machine Learning;    Deep learning;    Transfer learning;    Multiple myeloma;    Alzheimer’s disease;   

DOI  :  10.1186/s13073-022-01012-2

来源: Springer

【 摘 要 】

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information “impressions,” which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer’s disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19high myeloma cells associated with progression. Availability: https://github.com/tsteelejohnson91/DEGAS.

【 授权许可】

CC BY